Nobody expects that, obviously, but such solutions do occasionally get accepted (by humans). Beyond a certain basic level of competency, a lot of interviews, including LC-based ones, are about how good the candidate is at story telling and/or explaining their thought process.
You are correct - unless there is an actual need for exogenous testosterone, you should avoid TRT. However, natural T levels will drop with age, and it's not too uncommon for men to hop on in their 40's or later. Sure you'll need to take it for the rest of your life, but it's generally worth the improvement of quality of life.
You're off by an order of magnitude - $0.30 of $10 is 3%, which overshadows the 0.8% by quite a bit. Although I agree that $10 is most likely a low estimate, I'd expect $0.30 per charge to have a very non-trivial impact.
For further in-depth reading, I recommend the book "Range: Why Generalists Triumph in a Specialized World". It explores the generalist vs specialist dichotomy within various fields (e.g. sports, music, engineering) and compares the successes and contributions between the two. As you might guess from the title, the book reaches a very similar conclusion - for spaces involving unstructured problem solving such as engineering, breadth lends itself better to creativity and achieving breakthroughs.
> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
Is "data structures" the correct term here? Assuming I'm not misinterpreting, the usage of "data structures" can be misleading - one usually thinks of things like BST's and hash tables, which are inherently tied to algorithms. I feel like "data modeling" better captures the intended meaning here.
